Bibliographic record
Abstract
Before implementing a new therapy, we should ascertain the benefits and risks of the therapy and assure ourselves that the resources consumed during the intervention will not be exorbitant. In the hierarchy of research designs, the results of randomized controlled trials, especially if systematically reviewed, are considered the highest level of evidence. We suggest a three-step approach to using an article from the medical literature to guide your patient care. We recommend that readers ask whether the study can provide valid results, review the results, and consider how the results can be applied to patient care. Given the time constraints of busy surgical practices and surgical training programs, applying this analysis to every relevant article will be challenging. However, the basis of this process is essentially what we all do many times each week when making decisions about whether and how to treat patients. Making this process explicit with guidelines to assess the strength of the available evidence can serve to improve patient care. It also allow us to defend therapeutic interventions based on available evidence and not anecdote.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.300 | 0.747 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.020 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".